无人机多光谱遥感反演抽穗期冬小麦土壤含水率研究
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摘要
及时获取田间作物的土壤水分,对指导精准灌溉有重要意义。以抽穗期冬小麦为研究对象,采用低空无人机搭载六波段多光谱相机获取其冠层光谱反射率,并与参考点光谱反射率求差得差值反射率(DR),不同深度的土壤含水率(0~10、0~20、0~30、0~40、0~60 cm)与参考点土壤含水率同样求差得差值土壤含水率(DSM),对DR与DSM进行相关性分析,分别建立2者的一元线性模型和多元线性回归模型并验证。结果表明:一元模型中,40 cm深度的优于60 cm,20 cm的预测效果不佳;多元模型中,宽行距中40 cm深度的最优,建模R2和验证R2均达到了0.9以上,预测均方根误差仅为0.016。该研究可大面积快速获取田间土壤水分,为精准灌溉提供一定的理论依据。
The timely acquisition of soil moisture is of great significance to guide precision irrigation. Winter wheat at heading stage is studied in this paper. The spectral information of winter wheat canopy is collected by an unmanned aerial vehicle( UAV) equipped with a multi-spectral camera of six bands aiming at getting the difference( DR) between the spectral reflectance of reference point and that of each plot.Then,the difference( DSM) between the soil moisture content of reference point and that of each plot is calculated in the similar way.Through analyzing the relationship between DR and DSM,the single linear models and multivariate linear regression models are established and validated. The results show that the model of 40 cm is better than that of 60 cm in the single models and the model of 40 cm in wide spacing is the best with R2( determination coefficient) up to 0.9 and the prediction of RMSE( Root Mean Square Error) only 0.016 in the multivariate models. The study can quickly obtain soil moisture content in field to provide some theoretical basis for precision irrigation.
The timely acquisition of soil moisture is of great significance to guide precision irrigation. Winter wheat at heading stage is studied in this paper. The spectral information of winter wheat canopy is collected by an unmanned aerial vehicle( UAV) equipped with a multi-spectral camera of six bands aiming at getting the difference( DR) between the spectral reflectance of reference point and that of each plot.Then,the difference( DSM) between the soil moisture content of reference point and that of each plot is calculated in the similar way.Through analyzing the relationship between DR and DSM,the single linear models and multivariate linear regression models are established and validated. The results show that the model of 40 cm is better than that of 60 cm in the single models and the model of 40 cm in wide spacing is the best with R2( determination coefficient) up to 0.9 and the prediction of RMSE( Root Mean Square Error) only 0.016 in the multivariate models. The study can quickly obtain soil moisture content in field to provide some theoretical basis for precision irrigation.
引文
[1]汪志农.灌溉排水工程学[M].2版.北京:中国农业出版社,2009.
[2]张智韬,陈俊英,刘俊民,等.TM6对遥感主成分分析监测土壤含水率的影响[J].节水灌溉,2010,(4):16-19.
[3]张智韬,李援农,杨江涛,等.遥感监测土壤含水率模型及精度分析[J].农业工程学报,2008,(8):152-156.
[4]George P,Petropoulos G I.Surface soil moisture retrievals from remote sensing[J].Physics and Chemistry of the Earth,2015,83-84:36-56.
[5]Hatanaka T N A N R.Estimation of available moisture holding capacity of upland soils using Landsat TM data[J].Soil Science and Plant Nutrition,1995,41(3):577-586.
[6]James Cashiona V L D B.Microwave remote sensing of soil moisture evaluation[J].Journal of Hydrology,2005,307(1-4):242-253.
[7]刘建刚,赵春江,杨贵军,等.无人机遥感解析田间作物表型信息研究进展[J].农业工程学报,2016,(24):98-106.
[8]李德仁,李明.无人机遥感系统的研究进展与应用前景[J].武汉大学学报(信息科学版),2014,(5):505-513.
[9]李勇志,支晓栋,唐海龙,等.无人机遥感技术在农业中的发展与应用[J].安徽农业科学,2015,(25):350-351.
[10]Berni J A J,Zarco-Tejada P J,Suarez L,et al.Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle[J].IEEE Transactions on Geoscience&Remote Sensing,2009,47(3):722-738.
[11]Hassanesfahani L,Torresrua A,Jensen A,et al.Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks[J].Remote Sensing,2015,7(3):2 627-2 646.
[12]田明璐,班松涛,常庆瑞,等.基于低空无人机成像光谱仪影像估算棉花叶面积指数[J].农业工程学报,2016,(21):102-108.
[13]田明璐,班松涛,常庆瑞,等.基于无人机成像光谱仪数据的棉花叶绿素含量反演[J].农业机械学报,2016,(11):285-293.
[14]韩文霆,李广,苑梦婵,等.基于无人机遥感技术的玉米种植信息提取方法研究[J].农业机械学报,2017,(1):139-147.
[15]裴浩杰,冯海宽,李长春,等.基于综合指标的冬小麦长势无人机遥感监测[J].农业工程学报,2017,(20):74-82.
[16]徐莉平,刘钰,张建丰,等.冬小麦抽穗期土壤水分对生理指标影响的试验研究[J].水资源与水工程学报,2013,(3):23-25.
[17]李卫国,王纪华,赵春江,等.冬小麦抽穗期长势遥感监测的初步研究[J].江苏农业学报,2007,(5):499-500.
[18]Loague K,Green R E.Statistical and graphical methods for evaluating solute transport models:overview and application[J].Journal of Contaminant Hydrology,1991,7(1-2):51-73.
[19]Willmott C J.Some comments on the evaluation of model performance[J].Bulletin of the American Meteorological Society,1982,63(11):1 309-1 369.
[20]于振文.作物栽培学各论:北方本[M].2版.北京:中国农业出版社,2013.
[2]张智韬,陈俊英,刘俊民,等.TM6对遥感主成分分析监测土壤含水率的影响[J].节水灌溉,2010,(4):16-19.
[3]张智韬,李援农,杨江涛,等.遥感监测土壤含水率模型及精度分析[J].农业工程学报,2008,(8):152-156.
[4]George P,Petropoulos G I.Surface soil moisture retrievals from remote sensing[J].Physics and Chemistry of the Earth,2015,83-84:36-56.
[5]Hatanaka T N A N R.Estimation of available moisture holding capacity of upland soils using Landsat TM data[J].Soil Science and Plant Nutrition,1995,41(3):577-586.
[6]James Cashiona V L D B.Microwave remote sensing of soil moisture evaluation[J].Journal of Hydrology,2005,307(1-4):242-253.
[7]刘建刚,赵春江,杨贵军,等.无人机遥感解析田间作物表型信息研究进展[J].农业工程学报,2016,(24):98-106.
[8]李德仁,李明.无人机遥感系统的研究进展与应用前景[J].武汉大学学报(信息科学版),2014,(5):505-513.
[9]李勇志,支晓栋,唐海龙,等.无人机遥感技术在农业中的发展与应用[J].安徽农业科学,2015,(25):350-351.
[10]Berni J A J,Zarco-Tejada P J,Suarez L,et al.Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle[J].IEEE Transactions on Geoscience&Remote Sensing,2009,47(3):722-738.
[11]Hassanesfahani L,Torresrua A,Jensen A,et al.Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks[J].Remote Sensing,2015,7(3):2 627-2 646.
[12]田明璐,班松涛,常庆瑞,等.基于低空无人机成像光谱仪影像估算棉花叶面积指数[J].农业工程学报,2016,(21):102-108.
[13]田明璐,班松涛,常庆瑞,等.基于无人机成像光谱仪数据的棉花叶绿素含量反演[J].农业机械学报,2016,(11):285-293.
[14]韩文霆,李广,苑梦婵,等.基于无人机遥感技术的玉米种植信息提取方法研究[J].农业机械学报,2017,(1):139-147.
[15]裴浩杰,冯海宽,李长春,等.基于综合指标的冬小麦长势无人机遥感监测[J].农业工程学报,2017,(20):74-82.
[16]徐莉平,刘钰,张建丰,等.冬小麦抽穗期土壤水分对生理指标影响的试验研究[J].水资源与水工程学报,2013,(3):23-25.
[17]李卫国,王纪华,赵春江,等.冬小麦抽穗期长势遥感监测的初步研究[J].江苏农业学报,2007,(5):499-500.
[18]Loague K,Green R E.Statistical and graphical methods for evaluating solute transport models:overview and application[J].Journal of Contaminant Hydrology,1991,7(1-2):51-73.
[19]Willmott C J.Some comments on the evaluation of model performance[J].Bulletin of the American Meteorological Society,1982,63(11):1 309-1 369.
[20]于振文.作物栽培学各论:北方本[M].2版.北京:中国农业出版社,2013.